Data Privacy in the Age of AI: How Dynamics 365 Handles Regulatory Challenges
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P117Keywords:
Data Privacy, AI Ethics, Microsoft Dynamics 365, GDPR, Compliance, Data Governance, Regulatory Challenges, Machine Learning, Cloud SecurityAbstract
As the world is being more and more influenced by artificial intelligence, data privacy has turned into both a central issue for the strategy of a company and a problem for regulation. The paper is about the way that Microsoft Dynamics 365 combines its advanced AI-driven automation with the observance of strict compliance to global data protection frameworks such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA). It looks at how Dynamics 365’s data governance structure, privacy-by-design approach, and security measures based on the cloud contribute to keeping user trust and providing legal accountability in different regions. Microsoft’s approaches for the integration of AI ethics, e.g. the use of data in a responsible way, transparency of the model, and alleviation of bias, are discussed in the study which is in relation to their machine learning and predictive analytics modules. The paper also mentions the platform’s automated compliance reporting and consent management tools which facilitate adherence to the ever-changing regulatory standards.The paper finds that Dynamics 365 manages to keep a balance between smart automations and the maintenance of privacy so that the organizations are allowed to obtain the actionable insights without the rights of the individuals or data integrity being violated. For businesses, the consequences are double: on the one hand, the responsible use of AI as a lever leads to the improvement in operations and the increase of the trust of customers; on the other hand, it makes it possible to establish a measurable model of ethical compliance in the digital economy which can be scaled. This paper is part of the ongoing debate about how AI innovation could be in line with data protection rules. It offers a roadmap for enterprises that have to deal with the intersection of automation, governance, and global regulation.
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